Sub-optimal multiuser detector using an improved transiently chaotic neural network

This paper proposes a sub-optimal multiuser detector (MUD) algorithm for CDMA system based on an improved transiently chaotic neural network (ITCNN) which introduces a wavelet function into the activation function of the transiently chaotic neural network, and gives a concrete model of the MUD after appropriate transformations and mappings. The proposed neural network makes use of the wavelet and chaotic simulated annealing parameters of the recurrent neural network to control the network evolving behavior so that the network has richer and more flexible dynamics rather than conventional neural networks, so that it can be expected to have much powerful ability to search for globally optimal or sub-optimal solutions, and can refrain from the serious local optimal problem of Hopfield-type neural networks. Simulation experiments have been performed to show the effectiveness and validation of the proposed method for MUD problem.